The background description provided here is for the purpose of generally presenting the context of the disclosure. Work of the presently named inventors, to the extent it is described in this background section, as well as aspects of the description that may not otherwise qualify as prior art at the time of filing, are neither expressly nor impliedly admitted as prior art against the present disclosure.
Modern vehicle dynamic control systems, such as traction control systems (TCS), anti-lock braking systems (ABS), and electronic stabilization programs (ESP), have improved the safety of vehicles. The performance of these dynamic control systems is reliant to some degree on the accuracy of various vehicle parameters, such as the vehicle velocity, which are often estimated or determined based on input from one or more vehicle sensors.
The determination of vehicle velocity has been the subject of various papers and patent applications. Direct measurement of the longitudinal vehicle velocity may be too expensive and/or impractical for vehicle applications. Typical methods for determining vehicle velocity therefore fall into two groups: a first group that uses wheel speed and vehicle body acceleration to directly determine vehicle velocity; and a second group that estimates vehicle velocity based on a vehicle model to indirectly determine vehicle velocity.
With regard to methodologies falling into the first group, it is known to use maximum wheel speed for velocity estimation when the vehicle is braking and to use the minimum wheel speed when the vehicle is in a “traction mode”. Such practices are known as “best wheel methods” and can be employed to very rapidly determine the vehicle velocity. One drawback to the “best wheel methods” concerns the noise that may be present in the measurement of wheel speed. The accuracy with which the vehicle velocity is determined will vary based on the level of noise in the wheel speed signals.
Another methodology falling into the first group involves the identification of a reliable wheel speed, checking the vehicle body acceleration, and using a weighted average of the wheel speed and the integration of the vehicle body acceleration to obtain an estimate of the vehicle velocity. The accuracy of this method is dependent to some degree on the bias of the accelerometer that senses vehicle body acceleration and on the wheel radius measurement. Time integration of the longitudinal acceleration accumulates sensor bias, causing the estimation to drift. Another source of error is the gravitational component of acceleration acting in the direction of the road slope, distorting the acceleration measurement.
Additionally, the impact on vehicle velocity of accelerometer bias and wheel radius variation can change in different driving scenarios. Proposed solutions employ a weighted average method that uses feedback from the accelerometer bias and wheel radius offset, or data obtained through analysis of global positioning system (GPS) signals. The accuracy with which such methodologies determine vehicle velocity will vary based on noise associated with the derivative of wheel speed. A Kalman filter may be employed to determine the weighted average, but is not thought to be practical because the calculation is relatively complex and slow.
With regard to methodologies falling within the second group, a kinematic model can be used to estimate vehicle velocity using inputs of vehicle body acceleration and the rotational speed of the vehicle's four wheels. While this method can perform well in some situations, the results tend to be sensitive to signal noise and the location of the sensors.
Another methodology falling within the second group uses a tire model that provides a tire force estimation. While such methodologies are typically less sensitive (or even insensitive) to noise, the methodology includes a “non-linear situation” in which estimation error may be undesirably large.
While velocity estimation relies on wheel speeds, in some driving scenarios the wheel speed measurements of some wheels are not reliable, such as when a wheel is slipping with respect to the road surface. An adaptive Kalman filter has been used in an attempt to solve the slipping-wheel problem. However, in challenging conditions, such as when all four wheels are slipping, the adaptive Kalman filter may not provide satisfactory results.
In conclusion, each approach has drawbacks. Using wheel speed, error accrues due to slipping wheels, wheel radius variation, and offset of wheels from the vehicle's center of gravity. Integration of acceleration data is problematic if the initial velocity value is imprecise, and errors accumulate due to accelerometer bias and non-zero road slope. Vehicle/tire models are subject to modeling error, especially for non-linear models. Accordingly, there remains a need in the art to more accurately and reliably determine vehicle velocity.